姓名：艾清遥

职称：助理教授

电子邮件： aiqy@tsinghua.edu.cn

URL：https://aiqingyao.org/

教育背景

工学学士 (计算机科学与技术), 122cc太阳集成游戏, 中国, 2014;

Ph.D. (Computer Science), University of Massachusetts Amherst, USA, 2019.

工作履历

2019.9-2022.6- University of Utah, Tenure-Track Assistant Professor

2022.8- 122cc太阳集成游戏助理教授

社会兼职

2020-2022 CIKM 资深程序委员会成员

2021 NAACL-HLT 领域主席

2022 EMNLP 领域主席

研究领域

信息检索， 机器学习，自然语言处理

研究概况

主要研究领域集中在信息检索、机器学习以及自然语言处理研究方面。重点研究方向为智能信息检索系统的研究与设计， 包括信息表示学习模型，机器学习排序模型，无偏优化算法，可解释性、公平性检索模型等。在高水平信息检索领域会议期刊如SIGIR、TOIS、CIKM、WWW等发表文章五十余篇，曾担任NACCL’21、EMNLP‘22领域主席，CIKM’20/21/22资深程序委员，及SIGIR、WWW、ACL、AAAI、TOIS、TKDE等著名国际会议和期刊的程序委员及审稿人。曾主持亚马逊研究基金等研究工作，2021年获Google Research Scholar Award，2022年获国家人才计划支持。

奖励与荣誉

122cc太阳集成游戏计算机科学与技术系钟士模奖学金 (2012);

UMass CICS Accomplishments in Search & Mining Awards (2017);

Google Research Scholar Award (2021);

学术成果

(完整列表请参见：https://aiqingyao.org/)

[1] Yixiao Ma, Qingyao Ai, Yueyue Wu, Yunqiu Shao, Yiqun Liu, Min Zhang and Shaoping Ma. Incorporating Retrieval Information into the Truncation of Ranking Lists in the Legal Domain. The 45th International ACM SIGIR conference on Research and Development in Information Retrieval 2022.

[2] Tao Yang, Chen Luo, Hanqing Lu, Parth Gupta, Bing Yin, Qingyao Ai. Can clicks be both labels and features? Unbiased Behavior Feature Collection and Uncertainty-aware Learning to Rank. the 45th International ACM SIGIR conference on Research and Development in Information Retrieval 2022.

[3] Zhenduo Wang, Qingyao Ai. Simulating and Modeling the Risk of Conversational Search. ACM Transactions on Information Systems 2022

[4] Anh Tran, Tao Yang, and Qingyao Ai. ULTRA: An Unbiased Learning To Rank Algorithm Toolbox. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management 2021

[5] Qingyao Ai* and Lakshmi Narayanan R.*. Model-agnostic vs. Model-intrinsic Interpretability for Explainable Product Search. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management 2021

[6] Zhichao Xu, Hansi Zeng, Qingyao Ai. Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation. In Proceedings of the 7th ACM International Conference on the Theory of Information Retrieval 2021

[7] Keping Bi, Qingyao Ai, W. Bruce Croft. Asking Clarifying Questions Based on Negative Feedback in Conversational Search. In Proceedings of the 7th ACM International Conference on the Theory of Information Retrieval 2021

[8] Keping Bi, Qingyao Ai, W. Bruce Croft. A Review-based Transformer Model for Personalized Product Search. In the Proceedings of the 44th International ACM SIGIR conference on Research and Development in Information Retrieval 2021

[9] Zhenduo Wang, Qingyao Ai. Controlling the Risk of Conversational Search via Reinforcement Learning. Accepted in Proceedings of the 30th International Conference on World Wide Web 2021

[10] Tao Yang, Qingyao Ai. Maximizing Marginal Fairness for Dynamic Learning to Rank. Accepted in Proceedings of the 30th International Conference on World Wide Web 2021

[11] Hansi Zeng, Zhichao Xu, Qingyao Ai. A Zero Attentative Relevance Matching Network for Review Modeling in Recommender System. in Proceedings of the 43rd European Conference on Information Retrieval 2021

[12] Qingyao Ai, Tao Yang, Huazheng Wang, Jiaxin Mao. Unbiased Learning to Rank: Online or Offline? ACM Transactions on Information Systems 2021

[13] Zhichao Xu, Yi Han, Yongfeng Zhang, and Qingyao Ai. E-commerce Recommendation with Weighted Expected Utility. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management 2020

[14] Tao Yang, Shikai Fang, Shibo Li, Yulan Wang, and Qingyao Ai. Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management 2020

[15] Liang Pang, Jun Xu, Qingyao Ai, Yanyan Lan, Xueqi Cheng, and Jirong Wen. Setrank: Learning a permutation-invariant ranking model for information retrieval. In the Proceedings of the 43rd International ACM SIGIR conference on Research and Development in Information Retrieval 2020

[16] Keping Bi, Qingyao Ai, W. Bruce Croft. A Transformer-based Embedding Model for Personalized Product Search. In the Proceedings of the 43rd International ACM SIGIR conference on Research and Development in Information Retrieval 2020

[17] Qingyao Ai, Yongfeng Zhang, Keping Bi, W. Bruce Croft. Explainable Product Search with a Dynamic Relation Embedding Model. ACM Transactions on Information Systems 2019

[18] Keping Bi, Qingyao Ai, Yongfeng Zhang and W. Bruce Croft. Conversational Product Search Based on Negative Feedback. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management 2019

[19] Qingyao Ai, Daniel Hill, Vishy Vishwanathan and W. Bruce Croft. A Zero Attention Model for Personalized Product Search. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management 2019

[20] Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Maarten de Rijke, Qingyao Ai, Yufei Huang, Min Zhang and Shaoping Ma. Improving Web Image Search with Contextual Information. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management 2019

[21] Ruey-Cheng Chen, Qingyao Ai, Gaya Jayasinghe, W. Bruce Croft. Correcting for Recency Bias in Job Recommendation. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management 2019

[22] Qingyao Ai, Xuanhui Wang, Sebastian Bruch, Nadav Golbandi, Michael Bendersky and Marc Najork. Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks. In Proceedings of the 5th ACM International Conference on the Theory of Information Retrieval 2019

[23] Jiafeng Guo, Yixing Fan, Liang Pang, Liu Yang, Qingyao Ai, Hamed Zamani, Chen Wu, W. Bruce Croft, Xueqi Cheng. A Deep Look into Neural Ranking Models for Information Retrieval. In Information Processing and Management. July 9, 102067.

[24] Yukun Zheng, Yiqun Liu, Zhen Fan, Cheng Luo, Qingyao Ai, Min Zhang, Shaoping Ma. Investigating Weak Supervision in Deep Ranking.In Data and Information Management.

[25] Keping Bi, Qingyao Ai, W. Bruce Croft. Iterative Relevance Feedback for Answer Passage Retrieval with Passage-level Semantic Match. In Proceedings of the 41st European Conference on Information Retrieval 2019

[26] Yongfeng Zhang, Xu Chen, Qingyao Ai, Liu Yang, W. Bruce Croft. Towards Conversational Search and Recommendation: System Ask, User Respond. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management 2018

[27] Qingyao Ai, Vahid Azizi, Xu Chen, Yongfeng Zhang.Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation. In Algorithms, Special Issue Collaborative Filtering and Recommender Systems 2018, 11(9)

[28] Qingyao Ai, Keping Bi, Cheng Luo, Jiafeng Guo, W. Bruce Croft. Unbiased Learning to Rank with Unbiased Propensity Estimation. In Proceedings of the 41th International ACM SIGIR conference on Research and Development in Information Retrieval 2018

[29] Qingyao Ai, Keping Bi, Jiafeng Guo, W. Bruce Croft. Learning a Deep Listwise Context Model for Ranking Refinement. In Proceedings of the 41th International ACM SIGIR conference on Research and Development in Information Retrieval 2018

[30] Qingyao Ai, Brendan O'Connor, W. Bruce Croft. A Neural Passage Model for Ad-hoc Document Retrieval. In Proceedings of 40th European Conference on Information Retrieval 2018